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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (7): 983-999    DOI: 10.1007/s40333-024-0020-4     CSTR: 32276.14.s40333-024-0020-4
Research article     
Predicting potential invasion risks of Leucaena leucocephala (Lam.) de Wit in the arid area of Saudi Arabia
Haq S MARIFATUL1,*(), Darwish MOHAMMED1, Waheed MUHAMMAD1, Kumar MANOJ2, Siddiqui H MANZER3, Bussmann W RAINER1,4
1Department of Ethnobotany, Institute of Botany, Ilia State University, Tbilisi 0162, Georgia
2The Centre of Excellence on Sustainable Land Management (CoE-SLM), Indian Council of Forestry Research and Education, Dehradun 248006, India
3Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
4Department of Botany, State Museum for Natural History, Karlsruhe 76133, Germany
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Abstract  

The presence of invasive plant species poses a substantial ecological impact, thus comprehensive evaluation of their potential range and risk under the influence of climate change is necessary. This study uses maximum entropy (MaxEnt) modeling to forecast the likelihood of Leucaena leucocephala (Lam.) de Wit invasion in Saudi Arabia under present and future climate change scenarios. Utilizing the MaxEnt modeling, we integrated climatic and soil data to predict habitat suitability for the invasive species. We conducted a detailed analysis of the distribution patterns of the species, using climate variables and ecological factors. We focused on the important influence of temperature seasonality, temperature annual range, and precipitation seasonality. The distribution modeling used robust measures of area under the curve (AUC) and receiver-operator characteristic (ROC) curves, to map the invasion extent, which has a high level of accuracy in identifying appropriate habitats. The complex interaction that influenced the invasion of L. leucocephala was highlighted by the environmental parameters using Jackknife test. Presently, the actual geographic area where L. leucocephala was found in Saudi Arabia was considerably smaller than the theoretical maximum range, suggesting that it had the capacity to expand further. The MaxEnt model exhibited excellent prediction accuracy and produced reliable results based on the data from the ROC curve. Precipitation and temperature were the primary factors influencing the potential distribution of L. leucocephala. Currently, an estimated area of 216,342 km2 in Saudi Arabia was at a high probability of invasion by L. leucocephala. We investigated the potential for increased invasion hazards in the future due to climate change scenarios (Shared Socioeconomic Pathways (SSPs) 245 and 585). The analysis of key climatic variables, including temperature seasonality and annual range, along with soil properties such as clay composition and nitrogen content, unveiled their substantial influence on the distribution dynamic of L. leucocephala. Our findings indicated a significant expansion of high risk zones. High-risk zones for L. leucocephala invasion in the current climate conditions had notable expansions projected under future climate scenarios, particularly evident in southern Makkah, Al Bahah, Madina, and Asir areas. The results, backed by thorough spatial studies, emphasize the need to reduce the possible ecological impacts of climate change on the spread of L. leucocephala. Moreover, the study provides valuable strategic insights for the management of invasion, highlighting the intricate relationship between climate change, habitat appropriateness, and the risks associated with invasive species. Proactive techniques are suggested to avoid and manage the spread of L. leucocephala, considering its high potential for future spread. This study enhances the overall comprehension of the dynamics of invasive species by combining modeling techniques with ecological knowledge. It also provides valuable information for decision-making to implement efficient conservation and management strategies in response to changing environmental conditions.

Key words： area under the curve      invasive species      invasion risks      climate change      MaxEnt model     
Received: 29 January 2024      Published: 31 July 2024
Corresponding Authors: * Haq S MARIFATUL (E-mail: marifat.edu.17@gmail.com)
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Haq S MARIFATUL
Darwish MOHAMMED
Waheed MUHAMMAD
Kumar MANOJ
Siddiqui H MANZER
Bussmann W RAINER
Cite this article:

Haq S MARIFATUL, Darwish MOHAMMED, Waheed MUHAMMAD, Kumar MANOJ, Siddiqui H MANZER, Bussmann W RAINER. Predicting potential invasion risks of Leucaena leucocephala (Lam.) de Wit in the arid area of Saudi Arabia. Journal of Arid Land, 2024, 16(7): 983-999.

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http://jal.xjegi.com/10.1007/s40333-024-0020-4     OR     http://jal.xjegi.com/Y2024/V16/I7/983

Fig. 1 Leucaena leucocephala (Lam.) de Wit tree in Saudi Arabia. (a), invaded in degraded habitat; (b), mature tree with saplings; (c), seeds and fruit.
Fig. 2 Occurrence points of L. leucocephala in Saudi Arabia
Name of variable & description Code Unit Resolution Database
Annual mean temperature bio1 °C 30 arc s WorldClim
Mean diurnal range of temperature bio2 °C 30 arc s WorldClim
Isothermality ((Bio2/Bio7)×100%) bio3 % 30 arc s WorldClim
Temperature seasonality bio4 °C 30 arc s WorldClim
Maximum temperature of the warmest month bio5 °C 30 arc s WorldClim
Minimum temperature of the coldest month bio6 °C 30 arc s WorldClim
Temperature annual range bio7 °C 30 arc s WorldClim
Mean temperature of the wettest quarter bio8 °C 30 arc s WorldClim
Mean temperature of the driest quarter bio9 °C 30 arc s WorldClim
Mean temperature of the warmest quarter bio10 °C 30 arc s WorldClim
Mean temperature of the coldest quarter bio11 °C 30 arc s WorldClim
Annual precipitation bio12 mm 30 arc s WorldClim
Precipitation of the wettest month bio13 mm 30 arc s WorldClim
Precipitation of the driest month bio14 mm 30 arc s WorldClim
Precipitation seasonality (CV) bio15 % 30 arc s WorldClim
Precipitation of the wettest quarter bio16 mm 30 arc s WorldClim
Precipitation of the driest quarter bio17 mm 30 arc s WorldClim
Precipitation of the warmest quarter bio18 mm 30 arc s WorldClim
Precipitation of the coldest quarter bio19 mm 30 arc s WorldClim
Bulk density BD cg/cm3 30 arc s SoilGrids
Cation exchange capacity (pH=7) CEC mmol/kg 30 arc s SoilGrids
Volumetric fraction of coarse fragments (>2 mm) cfvo cm3/dm3 30 arc s SoilGrids
Clay content clay g/kg 30 arc s SoilGrids
Total nitrogen nitrogen cg/kg 30 arc s SoilGrids
Organic carbon density OCD μg/dm3 30 arc s SoilGrids
Soil pH phh2o - 30 arc s SoilGrids
Sand content sand g/kg 30 arc s SoilGrids
Silt content silt g/kg 30 arc s SoilGrids
Soil organic carbon SOC dg/kg 30 arc s SoilGrids
Land cover LC - 30 arc s http://www-modis.bu.edu/landcover
Population density PD - 30 arc s http://www.ornl.gov/sci/landscan
Table S1 Environmental predictors used in the species distribution model (SDM) for Leucaena leucocephala (Lam.) de Wit
Fig. 3 Pairwise correlation among biophysical and climatic variables in the distribution modeling of L. leucocephala. bio04, temperature seasonality; bio07, temperature annual range; bio15, precipitation seasonality; cfvo, volumetric fraction of coarse fragments (>2 mm). The abbreviations are the same in the following figures.
Fig. 4 Graphical representation of the receiver-operator characteristic (ROC) curve, which serves as a visualization of the predictive performance of the MaxEnt model. The precision of the model, quantified by an area under the curve (AUC) score of 0.96, is indicative of its ability to effectively discriminate between true positives and false positives. SD, standard errors.
Description Code Percentage of contribution (%)
Temperature seasonality bio04 41.30
Temperature annual range bio07 21.40
Precipitation seasonality bio15 12.70
Volumetric fraction of coarse fragments (>2 mm) cfvo 10.80
Clay content clay 7.20
Total nitrogen nitrogen 6.60
Table 1 Weighing the importance of various factors
Fig. 5 Analysis result of the MaxEnt model for L. leucocephala using the Jackknife test to evaluate the predictive effectiveness of environmental parameters. AUC, area under the curve.
Fig. 6 Parameters influencing the distribution of L. leucocephala. (a), bio04; (b), bio07; (c), bio15; (d), cfvo; (e), clay; (f), nitrogen.
Fig. 7 MaxEnt prediction result showing the distributed areas of L. leucocephala under current climate circumstances
Fig. 8 MaxEnt prediction result showing the distributed areas of L. leucocephala under several climate change scenarios. (a), shared socioeconomic pathways (SSPs) 245 in the 2050s; (b), SSPs 585 in the 2050s; (c), SSPs 245 in the 2070s; (d), SSPs 585 in the 2070s.
Climate change scenario Area of invasion of L. leucocephala
under different evaluated invasion risk classes (km2)		 Total area under invasion risks (km2)
No risk zones Low risk zones Moderate risk zones High risk zones
Current climate 1,404,315 205,320 304,235 216,342 725,897
SSPs 245 in the 2050s 1,319,352 218,303 335,737 256,820 810,860
Rate of change (%) -3.98 0.60 1.47 1.90 3.98
SSPs 585 in the 2050s 1,269,874 250,363 332,612 277,363 860,338
Rate of change (%) -6.31 2.11 1.33 2.86 6.31
SSPs 245 in the 2070s 1,311,040 232,259 324,597 262,316 819,172
Rate of change (%) -4.37 1.26 0.95 2.15 4.37
SSPs 585 in the 2070s 1,251,753 241,514 344,614 292,331 878,459
Rate of change (%) -7.16 1.69 1.89 3.56 7.16
Table 2 Area variation in L. leucocephala invasion under diverse climate change projections
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